1. Field of the Invention
The present invention relates to a method and apparatus for detecting and recognizing a face using subspace analysis, and more particularly, to a method and apparatus for detecting and recognizing a face using component-based principal component analysis (PCA)/independent component analysis (ICA).
2. Description of the Related Art
In the context of image processing and interpretation, a human face is an important factor in visual determination and identification. Since the early 1990's, extensive research into face recognition and facial expression interpretation has been conducted. Recently, MPEG-7 face descriptors have been proposed for face detection and identification in a series of images. The face descriptors offer rapid and accurate detection of the same images as those to be extracted, compared to conventional face recognition algorithms. The most challenging problem in face recognition is how to operate on combinations of images showing great changes in pose and illumination. Many different approaches to solving this problem have been developed.
Wang and Tan proposed a 2nd-order eigenface method for illumination-invariant face description. Kamei and Yamada extended the scope of work to use reliability factors in order to describe facial symmetry and changes in illumination in different environments. For face description, Nefian and Davies used an embedded Hidden Markov Model (eHMM) approach based on discrete cosine transform (DCT), and Kim et al. developed a 2nd-order PCA mixture model (PMM). Unfortunately, face descriptors excluding eHMM algorithm have been found to be inefficient in coping with pose changes. To effectively cope with pose changes, the eHMM algorithm involves using unobservable embedded states corresponding to each of a number of facial regions and segmenting an image into overlapping image blocks. However, HMM algorithms have a problem in that they tend to be dependent on local minimum values unless an initial solution approximates an overall minimum threshold.
A 2nd-order PCA method was proposed by Wang and Tan based on the observations that principal components corresponding to leading eigenvalues describe illumination changes rather than identity. First, PCA is performed on a set of training images. Images reconstructed from leading principal components corresponding to a first ten eigenvalues represent low-frequency components so the leading eigenvalues are sensitive to illumination variation. Then, the training images are obtained by subtracting the leading principal components from the reconstructed image. These images are called residual images and contain high-frequency components that are less sensitive to illumination variation. Lastly, the PCA is performed on the residual images obtained by subtracting illumination variant features. A 2nd-order PCA mixture model was introduced by Kim et al. to evaluate the probability distribution of various patterns in the facial image space. Kamei and Yamada added reliability factors in order to describe facial symmetry and changes illumination in different environments.
Barlett contended that ICA produces better basis images for face description than PCA, since ICA extracts important information from the facial image space containing higher order relationships among image pixels. This was proven by experimentally, as the experimental results on FERET face datasheet show. As shown in FIG. 1, an ICA representation is superior to a PCA representation, which is due to difference in selection of basis vectors. That is, when a data distribution is not Gaussian, PCA fails to accurately describe the data while ICA is able to appropriately describe the data since PCA basis vectors are orthogonal to each other.
To overcome problems with pose variation in face recognition, several component-based techniques for representing facial images as a set of facial components have been developed. In IEEE International Conference on Acoustics, Speech, and Signal Processing, Nefian and Davies used embedded HMM for face modeling. The facial components are internally modeled by HMM, and an optimization technique is used to match image blocks segmented from a facial image against the model. Similar face representation has been proposed by Wiskott et al. in IEEE Transactions on Pattern Analysis and Machine Intelligence, where they use a labeled graph based on a Gabor wavelet transform to represent facial components. This method also uses the phases of complex Gabor wavelet coefficients to accurately compute the positions of nodes for facial features. A component-based approach has also been developed by Heisele et al. in IEEE International Conference on Computer Vision. This approach involves detecting facial components independently to compensate for pose changes, and then using a support vector machine (SVM) geometrical configuration classifier to verify the geometrical configuration of the detected components against the model.
Typically, however, conventional PCA and ICA methods encode position relations among all pixels. Thus, statistically, changes in position configuration due to pose variation lead to significant changes in face representation.